Enhancing Creative Writing with Robot-LLM Integration: The Interplay of Embodiment, AI Creativity and User Engagement
Saved in:
| Title: | Enhancing Creative Writing with Robot-LLM Integration: The Interplay of Embodiment, AI Creativity and User Engagement |
|---|---|
| Language: | English |
| Authors: | Yuqing Liu (ORCID |
| Source: | British Journal of Educational Technology. 2026 57(5):1320-1347. |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 28 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Robotics, Artificial Intelligence, Collaborative Writing, Creative Writing, Undergraduate Students, Man Machine Systems, Program Effectiveness, Technology Uses in Education, Creativity, Foreign Countries |
| Geographic Terms: | China |
| DOI: | 10.1111/bjet.70071 |
| ISSN: | 0007-1013 1467-8535 |
| Abstract: | This study explores the impact of robot-LLM (Large Language Model) integration on collaborative creative writing, focusing on how embodiment and AI creativity influence various aspects of creative output. A total of 150 undergraduate students participated in a structured experimental design with five collaboration conditions: Human-Human (HH), Human-Computer with High-Creativity LLM (HC), Human-Robot with High-Creativity LLM (HR), Human-Robot with Low-Creativity LLM (RL) and Human-Computer with Low-Creativity LLM (CL). Creativity was assessed through expert ratings and computational analysis of originality, imagery, voice and semantic flow. The results revealed that while the Human-Robot (High-Creativity LLM) condition significantly enhanced originality, Human-Human and Human-LLM (text-based) collaborations excelled in imagery and voice. The study identified an 'embodiment paradox', where robot embodiment amplified creativity in high-creativity AI conditions, yet human collaboration remained superior in stylistic expression. Mediation analysis revealed that user engagement acted as a mediator, with embodiment compensating for low-creativity AI and amplifying the creative process with high-creativity AI. The findings have important implications for the design of collaborative AI systems, highlighting the need for a balanced integration of embodiment and AI creativity to optimize creative outcomes. This research contributes to our understanding of how human-robot-LLM collaborations can expand creative potential in writing, offering insights for future AI applications in educational and creative industries. |
| Abstractor: | As Provided |
| Entry Date: | 20260101 |
| Accession Number: | EJ1514404 |
| Database: | ERIC |
Be the first to leave a comment!
